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Multi-View Feature Transformation Based SVM+ for Computer-Aided Diagnosis of Liver Cancers With Ultrasound Images | IEEE Journals & Magazine | IEEE Xplore

Multi-View Feature Transformation Based SVM+ for Computer-Aided Diagnosis of Liver Cancers With Ultrasound Images


Abstract:

It is feasible to improve the performance of B-mode ultrasound (BUS) based computer-aided diagnosis (CAD) for liver cancers by transferring knowledge from contrast-enhanc...Show More

Abstract:

It is feasible to improve the performance of B-mode ultrasound (BUS) based computer-aided diagnosis (CAD) for liver cancers by transferring knowledge from contrast-enhanced ultrasound (CEUS) images. In this work, we propose a novel feature transformation based support vector machine plus (SVM+) algorithm for this transfer learning task by introducing feature transformation into the SVM+ framework (named FSVM+). Specifically, the transformation matrix in FSVM+ is learned to minimize the radius of the enclosing ball of all samples, while the SVM+ is used to maximize the margin between two classes. Moreover, to capture more transferable information from multiple CEUS phase images, a multi-view FSVM+ (MFSVM+) is further developed, which transfers knowledge from three CEUS images from three phases, i.e., arterial phase, portal venous phase, and delayed phase, to the BUS-based CAD model. MFSVM+ innovatively assigns appropriate weights for each CEUS image by calculating the maximum mean discrepancy between a pair of BUS and CEUS images, which can capture the relationship between source and target domains. The experimental results on a bi-modal ultrasound liver cancer dataset demonstrate that MFSVM+ achieves the best classification accuracy of 88.24±1.28%, sensitivity of 88.32±2.88%, specificity of 88.17±2.91%, suggesting its effectiveness in promoting the diagnostic accuracy of BUS-based CAD.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 27, Issue: 3, March 2023)
Page(s): 1512 - 1523
Date of Publication: 03 January 2023

ISSN Information:

PubMed ID: 37018255

Funding Agency:


References

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